Robot control device, robot system, and robot control method

ABSTRACT

A robot control device includes: a learned model created through learning work data composed of input and output data, the input data including states of a robot and the surroundings where humans operate the robot to perform a series of works, the output data including human operation corresponding to the case or movement of the robot caused thereby; a control data acquisition section that acquires control data by obtaining output data related to human operation or movement from the model, being presumed in response to and in accordance with the input data; a completion rate acquisition section acquiring a completion rate indicating to which progress level in the series of works the output data corresponds; and a certainty factor acquisition section that acquires a certainty factor indicating a probability of the presumption in a case where the model outputs the output data in response to the input data.

TECHNICAL FIELD

The present invention primarily relates to a robot control device thatcontrols a robot.

BACKGROUND ART

Conventionally known is a robot control device including a machinelearning device capable of creating a model related to a work movementof a robot. Patent Literature 1 (PTL 1) discloses a robot control deviceof this type.

PTL 1 discloses a robot system including a machine learning devicecapable of learning an optimal movement of a robot in taking out adisorderly-placed workpiece without intervention of human.

CITATION LIST Patent Literature

PTL 1: Japanese Patent Application Laid-Open No. 2017-30135

SUMMARY OF I NVENTION Technical Problem

Conventionally, an AI system uses a large amount of input and outputdata to presume a causal relationship between input and output, andcreates a model. When a learned model estimates output data based oninput data, a basis for the estimation is not explained to a user, butis treated as a so-called blackbox. When using the AI system forcontrolling a robot, therefore, the user is hardly given anything thatcan convince the user of an autonomous movement of the robot based on apresumed output of the AI system.

The present invention is made in view of the circumstances describedabove, and aims to provide a robot control device, and the like, thatcan make a robot movement based on an estimation by a learned model moreconvincing to a user.

Solution to Problem

The problem to be solved by the present invention is as above. Thefollowing describes solutions to the problem as well as advantageouseffects thereof.

A first aspect of the present invention provides a robot control devicehaving the following configuration. The robot control device includes alearned model, a control data acquisition section, a completion rateacquisition section, and a certainty factor acquisition section. Thelearned model is created through learning work data composed of inputdata and output data, the input data including states of a robot andsurroundings of the robot in a case where human operates the robot tocause the robot to perform a series of works, the output data includinga human operation corresponding to the case or a movement of the robotcaused by the human operation. The control data acquisition sectionacquires control data on the robot used to make the robot perform theworks, the acquisition being made by obtaining output data related to ahuman operation or a movement of the robot from the learned model, thehuman operation or the movement of the robot being presumed in responseto and in accordance with input data received by the learned model, theinput data being related to states of the robot and surroundings of therobot. The completion rate acquisition section acquires a completionrate indicating to which progress level in the series of works theoutput data outputted by the learned model corresponds. The certaintyfactor acquisition section acquires a certainly factor indicating aprobability of the presumption in a case where the learned model outputsthe output data in response to reception of the input data.

A second aspect of the present invention provides a robot control methodhaving the following configuration. The robot control method uses alearned model that denotes a model created through learning work datacomposed of input data and output data, the input data includingsituations of a robot and surroundings of the robot in a case wherehuman operates the robot to cause the robot to perform a series ofworks, the output data including a human operation corresponding to thecase or a movement of the robot caused by the human operation, the robotcontrol method including: a control data acquisition step of acquiringcontrol data on the robot used to make the robot perform the works, byobtaining output data related to a human operation or a movement of therobot from the learned model, the human operation or the movement of therobot being presumed in response to and in accordance with input datareceived by the learned model, the input data being related tosituations of the robot and surroundings of the robot; a completion rateacquisition step of acquiring a completion rate indicating to whichprogress level in the series of works the output data outputted by thelearned model corresponds; and a certainty factor acquisition step ofacquiring a certainty factor indicating a probability of the presumptionin a case where the learned model outputs the output data in response toreception of the input data.

Accordingly, based on the completion rate and the certainty factor thusacquired, the reason why the learned model has made such an output inresponse to the input can be inferred to some extent by the user. As aresult, the blackboxness which is conventionally inherent in the learnedmodel can be reduced, so that the user can apply the learned model to arobot control with a sense of conviction. In addition, the user canexamine the learning more appropriately, by using the acquiredcompletion rate and certainty factor as a clue.

Advantageous Effects of Invention

The present invention can provide a robot control device, and the like,that can make a robot movement based on an estimation by a learned modelmore convincing to a user.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 A block diagram showing an electrical configuration of a robotsystem according to an embodiment of the present invention

FIG. 2 A diagram showing exemplary robot movements that are to belearned by an AI system according to the embodiment

FIG. 3 A diagram for explanation of acquisition of a completion rate

FIG. 4 A diagram showing an exemplary change in the value of thecompletion rate in accordance with robot movements

FIG. 5 A diagram illustrating acquisition of a certainty factor

FIG. 6 A diagram showing an exemplary change in the value of thecertainty factor in accordance with robot movements

FIG. 7 A diagram showing soundness factors, and an example of a movementlog having evaluation values assigned

FIG 8 A diagram illustrating soundness factors after relearning

FIG 9 A diagram illustrating a process of starting an autonomousmovement of the robot in the middle of a series of movements

FIG 10 A diagram illustrating a process of terminating an autonomousmovement of the robot in the middle of a series of movements

FIG. 11 A diagram illustrating a process of transferring an autonomousmovement based on two different learned models

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the present invention will be describedwith reference to the drawings. FIG. 1 is a block diagram showing anelectrical configuration of a robot system 1 according to an embodimentof the present invention.

The robot system 1 is a system that performs works by using a robot 10.The works performed by the robot 10 are various works, examples of whichinclude assembling, processing, coating, washing, and the like.

The robot 10 is controlled with a model (learned model 43) that iscreated through machine learning of data, as will be detailed later. Therobot system 1, therefore, basically requires no assistance from a user,and is able to perform works autonomously. The robot 10 is able not onlyto perform works autonomously but also to perform works in accordancewith the user's operations. In the following, a state of the robot 10performing works autonomously may be referred to as “autonomousrunning”, and a state of the robot 10 performing works in accordancewith the user's operations may be referred to as “manual running”.

As shown in FIG. 1, the robot system 1 includes the robot 10 and a robotcontrol device 15. The robot 10 and the robot control device 15 areconnected to each other wirelessly or by wire, so that transmission andreception of signals therebetween are allowed.

The robot 10 includes an arm part attached to a pedestal. The arm parthas two or more joints, and each of the joints is provided with anactuator. The robot 10 moves the arm part by moving the actuators inaccordance with a movement instruction received from the outside.

Attached to the distal end of the arm part is an end effector that isselected in accordance with contents of a work. The robot 10 is able tomove the end effector in accordance with a movement instruction receivedfrom the outside.

A sensor for detecting movements of the robot 10, ambient environmentsof the robot 10, and the like, is attached to the robot 10. In thisembodiment, a movement sensor 11, a force sensor 12, and a camera 13 areattached to the robot 10.

The movement sensor 11 is disposed at each joint of the arm part of therobot 10, and detects a rotation angle or an angular velocity of eachjoint. The force sensor 12 detects a force that is received by the robot10 when the robot 10 moves. The force sensor 12 may be configured todetect a force acting on the end effector, or may be configured todetect a force acting on each joint of the arm part. The force sensor 12may be configured to detect a moment instead of or in addition to aforce. The camera 13 detects an image of a workpiece (the progress of awork on the workpiece) as a work object.

Data detected by the movement sensor 11 is movement data indicating amovement of the robot 10. Data detected by the force sensor 12 and thecamera 13 are ambient environment data indicating ambient environmentsof the robot 10. Data that integrates the movement data and the ambientenvironment data may hereinafter be referred to as state data. The statedata indicates states of the robot 10 and surroundings of the robot 10.

Hereinafter, the movement sensor 11, the force sensor 12, and the camera13 provided to the robot 10 may be collectively referred to as “statedetection sensors 11 to 13”, Data detected by the state detectionsensors 11 to 13 may be especially referred to as “sensor information”.The state detection sensors 11 to 13 may be provided in the surroundingsof the robot 10, instead of being attached to the robot 10.

The robot control device 15 includes a user interface section 20, amovement switching section (control data acquisition section) 30, an AIsection 40, an AI parameter acquisition section 50, a completion ratemonitoring section 56, a certainty factor monitoring section 57, a logidentification information generation section 58, a movement loggeneration section 60, and a movement log storage section 70.

To be specific, the robot control device 15 is a computer including aCPU, a ROM, a RAM, and a HDD. The computer includes a device, such as amouse, to be operated by the user. The computer preferably includes aGPU, because it allows learning through a neural network which will bedescribed later to be performed in a short time. The HDD stores aprogram for moving the robot control device 15. Cooperation of theabove-mentioned hardware and software allows the robot control device 15to function as the user interface section 20, the movement switchingsection 30, the AI section 40, the AI parameter acquisition section 50,the completion rate monitoring section 56, the certainty factormonitoring section 57, the log identification information generationsection 58, the movement log generation section 60, and the movement logstorage section 70.

The robot control device 15 may be implemented by a single computer, ormay be implemented by two or more computers operating in cooperation andcommunication with one another.

The user interface section 20 implements a user interface function ofthe robot control device 15. The user interface section 20 includes anoperation section 21, a display section 22, and an evaluation valuesetting section 23.

The operation section 21 is a device used to manually operate the robot10. The operation section 21 can be configured to have a lever, a pedal,and the like, for example.

The operation section 21 includes a sensor that detects an operatingposition of the operation section 21, though not shown. The operationsection 21 further includes a known operation force detection sensor.The operation force detection sensor detects a force (operation force)that the user applies to the operation section 21.

In a case where the operation section 21 is configured to be capable ofmoving in various directions, the operation force may be a valuecontaining the direction and magnitude of a force, as exemplified by avector. The operation force may be detected not only as a force (N)applied by the user but also as an acceleration, which is a value linkedwith the force (i.e., a value obtained by dividing the force appliedfrom the user by the mass of the operation section 21).

Hereinafter, the operation force that the user applies to the operationsection 21 may be especially referred to as “user operation force”. Theuser operation force that is outputted as a result of the user'soperating the operation section 21 is converted into a movementinstruction in the movement switching section 30, as will be describedlater.

The display section 22 is capable of displaying various types ofinformation in accordance with user's instructions. The display section22 may be a liquid crystal display, for example. The display section 22is disposed near the operation section 21.

In a case of the operation section 21 being remote from the robot 10,the display section 22 may display an image of the robot 10 andsurroundings of the robot 10.

The evaluation value setting section 23 is capable of setting anevaluation given by the user, with respect to a movement of the robot 10described in a movement log read out from the movement log storagesection 70. The movement log, and the like, will be described later.

The robot 10, the operation section 21, and the AI section 40 areconnected to the movement switching section 30. The movement switchingsection 30 receives the user operation force outputted by the operationsection 21 and a later-described presumed operation force outputted bythe AI section 40.

The movement switching section 30 outputs a movement instruction formoving the robot 10 to the robot 10 and to the AI section 40. Themovement switching section 30 includes a switching section 31 and aconversion section 32.

The switching section 31 is configured to output, to the conversionsection 32, one of the user operation force or the presumed operationforce received by the switching section 31. The switching section 31 isconfigured to output the user operation force or the presumed operationforce to the conversion section 32 based on a selection signalindicating which of the user operation force or the select operationforce is to be converted. In this manner, a state where the user movesthe robot 10 (manual running) and a state where the robot system 1causes the robot 10 to work autonomously (autonomous running) can beswitched. In the manual running, the robot 10 moves based on the useroperation force outputted by the operation section 21. In the autonomousrunning, the robot 10 moves based on the presumed operation forceoutputted by the AI section 40.

Whether the robot 10 is moved based on the user operation force or thepresumed operation force can be selected automatically in accordancewith whether or not the user is operating the operation section 21,based on a detection value from the above-described sensor that detectsoperations of the operation section 21. More specifically, when the useris substantially operating the operation section 21, the switchingsection 31 outputs the user operation force to the conversion section32, or otherwise the switching section 31 outputs the presumed operationforce to the conversion section 32.

The conversion section 32 converts either the user operation force orthe presumed operation force received from the switching section 31 intoa movement instruction for moving the robot 10, and outputs the movementinstruction to the robot 10 and to the AI section 40. The movementinstruction can also be called control data for controlling the robot10.

The AI section 40 includes the learned model 43 that is created in orderto cause the robot 10 to perform a series of works through autonomousmovements. The model may be in any form. In this embodiment, a modelbased on a neural network is adopted. The creation (especially theinitial creation) of the learned model 43 may be performed either in therobot control device 15 or in another computer.

The AI section 40 includes not only the learned model 43 but also a datainput section 41 and a presumed data output section 42.

The data input section 41 functions as an interface on the input side ofthe AI section 40. The data input section 41 receives sensor informationoutputted from the state detection sensors 11 to 13.

The presumed data output section 42 functions as an interface on theoutput side of the AI section 40. The presumed data output section 42 iscapable of outputting data based on a model that the AI section 40 hascreated through machine learning,

In this embodiment, the AI section 40 learns operations of the robot 10that are performed by the user via the operation section 21, and createsthe learned model 43. More specifically, the AI section 10 receives thesensor information from the state detection sensors 11 to 13 as well asthe operation force that the user has applied to the operation section21 at a time corresponding to the sensor information.

The learned model 43 adopted in the AI section 40 may be in any form.The learned model 43 of this embodiment is a neural network having ageneral configuration with an input layer, a hidden layer, and an outputlayer. In each of the layers, two or more units that mimic brain cellsare arranged. The hidden layer is disposed between the input layer andthe output layer, and the hidden layer includes an appropriate number ofintermediate units. Information flows through the input layer, thehidden layer, and the output layer in this order. The number of hiddenlayers is set as appropriate.

In this model, data (input data) received by the input layer is thesensor information mentioned above. As described above, the sensorinformation is data indicating the states of the robot 10 andsurroundings of the robot 10. Data (output data) outputted by the outputlayer is an operation force as a presumed user operation force. Thisoperation force can be considered as data indicating a presumed humanoperation.

Each input unit and each intermediate unit are coupled by a path throughwhich information flows. Each intermediate unit and each output unit arecoupled by a path through which information flows. In each of the paths,an influence (weight) that information from an upstream unit has on adownstream unit is set.

In a learning phase, the AI section 40 inputs sensor information to amodel, and an operation force outputted from the model is comparedagainst the user operation force (supervised learning). The AI section40 updates the above-mentioned weight by back propagation, which is aknown algorithm, such that an error obtained in the foregoing manner canbe reduced. Continuously performing this process can implement learning.

In a presuming phase after creation of the learned model 43, the AIsection 40 inputs sensor information to the learned model 43, and anoperation three outputted from the learned model 43 is outputted as apresumed operation force to the movement switching section 30.

In a case where the switching section 31 outputs the presumed operationforce received from the AI section 40 to the conversion section 32, themovement switching section 30 generates control data based on thepresumed operation force. In this case, the movement switching section30 functions as a control data acquisition section that acquires controldata for causing the robot 10 to perform a work based on the output fromthe AI section 40.

The user is able to make the AI section 40 create the learned model 43for use to cause the robot 10 to perform a series of works for insertinga workpiece into an opening of a member, for example.

To be specific, the user operates the operation section 21, to move therobot 10 in the following manner, for example. Referring to FIG. 2,movement A includes: making the robot 10 hold a workpiece; in thisstate, placing the workpiece above a member; and bringing the workpiececloser to a surface of the member. Movement B includes moving theworkpiece as it is, to bring the workpiece into contact with the surfaceof the member. Movement C includes moving the workpiece toward theposition of an opening. Here, in this movement of the workpiece, theworkpiece is kept in contact with the surface of the member. Movement Dincludes bringing an end portion of the workpiece into contact with aninner wall of the opening. Movement E includes inserting the workpieceinto the opening.

The user operates the robot 10 so as to make the robot 10 move in orderfrom movement A to movement E. The relationship between the sensorinformation and the user operation force during this process is learned,and thereby the AI section 40 is able to create the learned model 43capable of making the robot 10 autonomously move in order from movementA to movement E.

The AI parameter acquisition section 50 is able to acquire variousparameters obtained when the learned model 43 of the AI section 40outputs the presumed operation force, by requesting them from the AIsection 40. These parameters are parameters to which human can givemeanings in relation to the autonomous work of the robot 10. In manycases, the reason why the learned model 43 presumes an output based onan input cannot be explained. These parameters are therefore importantin that they provide a clue to making the user understand and convincedof the movement of the robot 10 presented by the learned model 43.

As these parameters, the AI parameter acquisition section 50 acquires acompletion rate, a certainty factor, and a soundness factor. The AIparameter acquisition section 50 correspondingly includes a completionrate acquisition section 51, a certainty factor acquisition section 52,and a soundness factor acquisition section 53.

The completion rate acquisition section 51 acquires a completion rate.The completion rate is a parameter used to evaluate to which progresslevel in a series of works the movement that the robot 10 performs basedon the output from the learned model 43 corresponds. In this embodiment,the completion rate takes a value in a range of 0 to 100. A value closerto 100 indicates a more progress in the series of works.

Referring to FIG. 3, calculation of the completion rate will bedescribed. In this embodiment, as shown in FIG. 3, the completion rateis calculated in consideration of clusters obtained by clustering thestates of the robot 10 that can be acquired chronologically and amovement history of the robot 10.

Each of the states of the robot 10 mentioned above can be expressed as amulti-dimensional vector (characteristic vector) containing the sensorinformation of the state detection sensors 11 to 13 and the presumedoperation force presumed by the learned model 43. The characteristicvector changes variously in the course of the robot 10 performing theseries of works. The characteristic vector may contain not only thevalues of the sensor information and the presumed operation force at thecurrent point of time but also a past history of the sensor informationand the presumed operation force.

Hereinafter, an integration of the states of the robot 10 andsurroundings of the robot 10 with a result of presumption that thelearned model 43 has made in accordance with the states of the robot 10and surroundings of the robot 10 may be called an aspect of the robot10. Data (aspect data) indicating an aspect of the robot 10 is used asthe characteristic vector mentioned above. The aspect data correspondsto an integration of input data and output data of the learned model 43.

Clustering is a type of unsupervised learning, and is a technique ofacquiring two or more clusters, each of which is a group of data pieceshaving similar characteristics, by learning distribution rules based ona large number of data pieces. As a clustering method, a knownnon-hierarchical clustering technique can be used as appropriate.

Aspects of the robot 10 have different characteristics for the differentmovements (movement A to movement E) described above. For example,characteristics in the state of movement A (i.e., aspect data acquiredin movement A) are different from characteristics in the state ofmovement B. Therefore, appropriately clustering the above-mentionedcharacteristic vectors serving as objects of the clustering can classifythe aspects of the robot 10 according to movements.

The AI section 10 uses a result of the clustering to calculate acompletion rate corresponding to the current aspect of the robot 10. Asshown in FIG. 3, completion rate values are set in advance so as toincrease stepwise and cumulatively according to the order of movementsindicated by respective clusters. Since the series of works performed bythe robot 10 can be expressed as characteristic vectors arranged in achronological order, information which is in this chronological ordercan be used to obtain a chronological order of the clusters.

The AI section 40 obtains, by computation, to which cluster thecharacteristic vector indicating the current aspect of the robot 10belongs, and outputs a completion rate corresponding to this cluster inresponse to a request from the AI parameter acquisition section 50. Towhich cluster the characteristic vector belongs can be identified by,for example, obtaining a distance between the center of gravity of eachcluster and the characteristic vector and selecting a cluster whosecenter of gravity is at the shortest distance from the characteristicvector.

As shown in FIG. 4, if the works by the robot 10 are advancing (that is,if the aspect of the robot 10 is transitioning properly), the value ofthe completion rate increases as time elapses. If the works by the robot10 are not advancing (for example, if a transition to a specific aspectis repeated), the value of the completion rate does not increase eventhough time elapses. The user, therefore, can easily grasp whether ornot the autonomous work by the robot 10 is advancing, by seeing a changein the completion rate. As a result, the user can easily find stagnationof the movement of the robot 10, and thus can take proper measures suchas correction of the movement.

The certainty factor acquisition section 52 acquires a certainty factor.The certainty factor is a parameter used to evaluate whether or not amovement of the robot 10 is probable (in other words, whether or not anoutput presumed by the learned model 43 is probable).

The learned model 43 of the AI section 40 has learned in advance thecorrespondence relationship of states of the robot 10 and surroundingsof the robot 10 to a user operation force applied by the user'soperation performed at that time. In other words, the learned model 43operates based on rules obtained from a large number of given states. Itis expected that a generalization ability that is inherent in a machinelearning model will allow the learned model 43 to output a properpresumed operation force even in an unknown situation. In this respect,however, if a person is thrown into a completely unfamiliar situationthat can be hardly predicted based on past experiences, it would not beeasy for the person to behave with certainty. In the same way, from theviewpoint of the learned model 43, the farther a state is from the givenstates that the learned model 43 has learned, the more difficult itwould be for the learned model 43 to be certain about a result of thepresumption. In this sense, the certainty factor indicates a probabilityof the presumption.

In this embodiment, in the AI section 40, a probabilistic discriminatorfor discriminating an aspect of the robot 10 is created by machinelearning. The probabilistic discriminator comprises two or moreprobabilistic discriminators according to the number of clusters thatare classified by the above-described clustering.

For example, the probabilistic discriminator corresponding to thecluster of movement A undergoes machine learning such that theprobabilistic discriminator outputs a value close to 100 if receiving acharacteristic vector classified into the cluster of movement A as aresult of the clustering, and outputs a value close to 0 if receiving acharacteristic vector classified into a cluster of another movement as aresult of the clustering. That is, if a probabilistic discriminatorhaving undergone learning receives a characteristic vector indicatingthe current aspect of the robot 10, the probabilistic discriminatoroutputs a value indicating whether or not the aspect is probablymovement A. This value can be considered as substantially representing aprobability (presumption probability) that the current aspect of therobot 10 is movement A. The probabilistic discriminators correspondingto the other clusters (the other movements B to E) also undergo learningin the same manner as above.

Inputting a characteristic vector to each of the two or moreprobabilistic discriminators makes it possible to obtain to which one ofmovements A to E the current situation is presumed to correspond and toobtain whether or not that presumption is probable, based on theprobabilistic discriminators.

In this embodiment, as shown in FIG. 5, the largest value amongpresumption probabilities outputted by the two or more probabilisticdiscriminators is used as the certainty factor. If the current aspect issimilar to a given aspect (in other words, an aspect that is classifiedinto any of movements A to E as a result of the clustering) of the robot10, the certainty factor has a large value. On the other hand, if thecurrent aspect is not similar to a given aspect of the robot 10, thecertainty factor has a small value.

As shown in FIG. 6, the user is able to evaluate whether or not amovement of the robot 10 is probable, by seeing the value of thecertainty factor during the series of works, for example. To bespecific, in a case of performing a movement that is not memorized bythe learned model 43, the value of the certainty factor decreases. Thisallows the user to grasp that a movement that has not been sufficientlylearned is included in the series of works. It may be conceivable that amovement assigned a low certainty factor is automatically detected bythe robot control device 15. In a case of performing a movement that ismemorized by the learned model 43, the value of the certainty factorincreases. Accordingly, the user is able to recognize that a movement ofthe robot 10 in an aspect agrees with a given movement.

The user is also able to confirm that a movement of the robot 10 hasreached a given state (e.g., any of movements A to E), by using thevalue of the certainty factor.

The soundness factor acquisition section 53 acquires a soundness factor.The soundness factor is a parameter indicating the degree to which astate recognized by the learned model 43 is preferable for the user. Inthis embodiment, the soundness factor takes a value in a range of 0 to100. A value closer to 100 indicates that the aspect is more preferablefor the user.

The soundness factor is a parameter used to artificially influence aprobability or weight, where the probability or weight represents thedegree of easiness in transitioning to each of branch movements branchedfrom a movement in the learned model 43.

As shown in FIG. 7, the learned model 43 is able to express a statetransition (in other words, a transition from a movement to a movement).At a branch point where a transition of movement can occur, the degreeof easiness in transitioning to each movement is substantiallyrepresented by a weight. In the example shown in FIG. 7, a weightrelated to a transition from movement B to movement X is 0.42, and aweight related to a transition from movement B to movement C is 0.57. Inthis example, therefore, a transition of movements A, B, C, and D iseasier than a transition of movements A, B, X, and Y.

Each movement of the robot 10 is assigned a soundness factor. Via thevalue of the soundness factor, the user is able to create the learnedmodel 43 with a transition of movements of the robot 10 (i.e., theprobability or weight mentioned above) adjusted, as will be detailedlater. Referring to FIG. 7, in an initial state, the values of soundnessfactors related to all the movements are 100. The user is able to createthe learned model 43 with a transition of movements of the robot 10adjusted, by lowering the value of the soundness factor related to anunpreferable movement.

The completion rate monitoring section 56 shown in FIG. 1 monitors acompletion rate acquired by the completion rate acquisition section 51mentioned above. As shown in FIG. 4, the completion rate monitoringsection 56 is able to detect a situation where the completion rateremains unchanged for a predetermined period, to thereby detectstagnation of a movement of the robot 10.

If the completion rate monitoring section 56 detects stagnation of amovement of the robot 10, the robot control device 15 may stopcontrolling the robot 10 and perform a process of ceasing a workperformed by the robot 10. This can provide a timeout function (afunction for abandoning the continuation of the work) that is based on aresult of monitoring by the completion rate monitoring section 56.

If the completion rate monitoring section 56 detects stagnation of amovement of the robot 10, the robot control device 15 may control therobot 10 such that a work having changed settings is applied from ahalfway point. This can provide a retry function that is based on aresult of monitoring by the completion rate monitoring section 56.

The certainty factor monitoring section 57 monitors a certainty factoracquired by the certainty factor acquisition section 52. The certaintyfactor monitoring section 57 constantly monitors the value of thecertainty factor, and thereby can detect a movement whose certaintyfactor value does not reach a predetermined value, as shown in FIG. 6.Thus, the certainty factor monitoring section 57 is able to detect amovement that has not been sufficiently learned (in other words, anaspect for which additional learning is highly necessary). To performadditional learning, the user can easily grasp at which point theadditional learning should be started, based on a result of monitoringby the certainty factor monitoring section 57.

The log identification information generation section (input dataidentification information generation section) 58 generates informationindicating data received by the learned model 43 when the certaintyfactor is equal to or less than a predetermined value. This allows theuser to easily grasp in which aspect insufficient learning occurs. Thisinformation may be, for example, a log ID which will be described later.

The movement log generation section 60 generates a movement log.Described in the movement log are various types of information obtainedwhen the robot 10 is autonomously moved. The movement log can contain,for example, the above-mentioned sensor information, presumed operationforce, movement instruction, and the like. In the movement log,diversified types of information are described for each aspect of therobot 10. Each of the aspects described in the movement log is givenidentification information (log ID) that can uniquely identify theaspect, though not shown.

The movement log may contain information related to at least any of thecompletion rate, the certainty factor, or the soundness factor outputtedby the AI parameter acquisition section 50. With this configuration, theuser is able to evaluate a movement of the robot 10 based on thecompletion rate, etc, contained in the movement log. For example, theuser can easily understand a trace of works performed by the robot 10from the viewpoint of the completion rate. The user can also easilyunderstand a similarity to a specific movement from the viewpoint of thecertainty factor.

The movement log storage section 70 stores the movement log generated bythe movement log generation section 60. The movement log thus stored canbe displayed by the display section 22 in response to the user'sappropriately operating the user interface section 20.

In this embodiment, the AI section 40 is able to re-create the learnedmodel 43 in consideration of an evaluation from the user. In thefollowing, re-creation of a learned model will be described withreference to FIG. 7, etc.

The robot 10 autonomously moves based on an output from the learnedmodel 43, and as a result, a movement log is obtained. Then, the usercan partially set an evaluation value to the movement log, as shown inFIG. 7. The evaluation value is a result of evaluating whether themovement of the robot 10 is good or bad, the evaluation being made fromthe viewpoint of the user. The setting can be made by using theevaluation value setting section 23 included in the user interfacesection 20.

FIG. 7 shows an example in which the evaluation value is set in themovement log. In FIG. 7, contents of the movement log are expressed in avery simplified manner. The user selects OK for a portion that the userhas evaluated as good, and selects NG for a portion that the user hasevaluated as bad. It is preferable that the value of the soundnessfactor be contained in the movement log, because it allows the user toselect OK or NG with reference to the value of the soundness factor.

After selecting the evaluation value, the user performs a predeterminedoperation to instruct that the learned model 43 be re-created. The AIsection 40 firstly lowers the value of a soundness factor related to amovement for which NG is selected as the evaluation value, whilemaintaining the value of a soundness factor related to a movement forwhich OK is selected as the evaluation value. Then, the learned model 43is re-created. In the re-created learned model 43, as shown in FIG. 8,the above-described weight, which indicates the degree of easiness intransitioning to a movement, is adjusted such that the probability oftransitioning to a movement having a higher soundness factor increasesand the probability of transitioning to a movement having a lowersoundness factor decreases. This makes it likely that a transitiondesirable for the user is preferentially selected. Consequently, thecustomizability is improved, and the learned model 43 that is moreconvincing to the user can be obtained.

Next, another example of how to utilize the completion rate and thecertainty factor will be described.

The robot control device 15 of this embodiment is able to deal with aseries of movements implemented by the single learned model 43 whilesubstantially separating a part of the movements from the rest of themovements.

First, a case of starting an autonomous movement in the middle of aseries of movements will be described with reference to FIG. 9. The usersets in advance a condition for the learned model 43 (in FIG. 9,indicated as the learned model X) to start an autonomous movement. Thecondition is set in the form of a completion rate. In an example shownin FIG. 9, the completion rate being 20 is set as the condition forstarting the autonomous movement.

Then, the user operates the operation section 21 to move the robot 10 inthe order of movements M1, M2, . . . . Movements M1, M2, . . . representa process of movements in manual running. At this time, the AI parameteracquisition section 50 requests, from the AI section 40, a completionrate and a certainty factor corresponding to each aspect (here, a useroperation force is used instead of a presumed operation force) of therobot 10.

In the aspect of movement M1, the obtained completion rate is 20, whichsatisfies the completion rate condition for starting the autonomousmovement. This means that the aspect of movement M1 is determined asbeing somewhat similar to the aspect of movement B (the movement atwhich the completion rate reaches 20) of the learned model X. Thecertainty factor is as low as 40, however. In this stage, therefore, anautonomous operation of the AI section 40 is not started.

In the aspect of next movement M2, the obtained completion rate is 20,which satisfies the completion rate condition for starting theautonomous movement. The certainty factor is as high as 80, and thus theprobability is good. Only when the completion rate satisfies thecondition and additionally the certainty factor is equal to or greaterthan a predetermined value like in the above-described case, the AIsection 40 starts the output based on the learned model 43, startingfrom movement B corresponding to this completion rate. In conjunctionwith this, the movement switching section 30 switches the control fromthe control based on the user operation force to the control based onthe presumed operation force. Thereafter, movements C, D, and F areperformed by the autonomous movement of the robot 10.

In this manner, it is possible to substantially take out only movementsB to E from the learned model 43 having learned to perform a series ofworks including movements A to E, to start a control in the middle ofthe series of works.

Next, a case of terminating an autonomous movement in the middle of aseries of movements will be described with reference to FIG. 10. Theuser sets in advance a condition for the learned model X to terminate anautonomous movement. The condition is set in the form of a completionrate. In an example shown in FIG. 10, the completion rate being 60 isset as the condition for terminating the autonomous movement.

The AI section 40 causes an autonomous movement of the robot 10 in theorder of movement A, movement B, . . . . In this process, the AIparameter acquisition section 50 requests, from the AI section 40, acompletion rate and a certainty factor corresponding to each aspect ofthe robot 10.

In the aspect of movement D, the obtained completion rate is 60, whichsatisfies the completion rate condition for terminating the autonomousmovement. The certainty factor is as high as 85, and thus theprobability is good. Only when the completion rate satisfies thecondition and additionally the certainty factor is equal to or greaterthan a predetermined value like in the above-described case, the AIsection 40 terminates the output based on the learned model 43, byending with movement D corresponding to this completion rate. Thus,movement E is not performed.

In this manner, it is possible to substantially take out only movementsA to D from the learned model 43 having learned to perform a series ofworks including movements A to E, to perform a control only up to themiddle of the series of works.

Coupling autonomous movements based on two different learned models 43will now be described with reference to FIG. 11.

In an example shown in FIG. 11, the robot control device 15 creates twodifferent learned models 43. In the following description, one of thelearned models 43 may be referred to as learned model X, and the otherof the learned models 43 may be referred to as learned model Y. Thelearned model X has learned in advance a series of works includingmovements O to Q. The learned model Y has learned in advance a series ofworks including movement S and subsequent movements.

Before coupling autonomous movements based on the two learned models 43,the robot control device 15 verifies termination of the autonomousmovement based on the learned model X and start of the autonomousmovement based on the learned model Y. The completion rate and thecertainty factor are used for this verification.

In the example shown in FIG. 11, with respect to the learned model X,the completion rate being 100 is set as a condition for terminating theautonomous movement. With respect to the learned model Y, the completionrate being 0 is set as a condition for starting the autonomous movement.

Based on the output from the learned model X, the AI section 40 causesan autonomous movement of the robot 10 in the order of movement O,movement P, . . . . In this process, the AI parameter acquisitionsection 50 requests, from the AI section 40, a completion rate and acertainty factor corresponding to each aspect of the robot 10, for eachof the learned models X and Y.

In the aspect of movement Q, as for the learned model X, the obtainedcompletion rate is 100, which satisfies the completion rate conditionfor terminating the autonomous movement. The certainty factor is as highas 85, and thus the probability is good. In the aspect of movement Q, asfor the learned model Y, the completion rate is 0, which satisfies thecompletion rate condition for starting an autonomous movement. Thecertainty factor is as high as 80, and thus the probability is good.

In this manner, the learned model 43, which outputs a presumed operationforce to be used for the autonomous movement, is switched on conditionthat the certainty factors in both of the learned models X and Y are atpredetermined levels or higher. At a timing of movement Q, the AIsection 40 switches the learned model 43 from the learned model X to thelearned model Y with a succession of the autonomous movement. As aresult, a series of movements, namely, movement O, movement P, movementQ (≈movement S), movement T, movement U, . . . is performed.

By using the values of the completion rate and the certainty factor, therobot control device 15 is able to transfer movements between twodifferent models, so that a series of works (coupled works) can beperformed by the robot 10.

As thus far described, the robot control device 15 according to thisembodiment includes the learned model 43, the movement switching section30, the completion rate acquisition section 51, and the certainty factoracquisition section 52. The learned model 43 is created through learningwork data composed of input data and output data. The input dataincludes situations of the robot 10 and surroundings of the robot 10 ina case where human operates the robot 10 to cause the robot 10 toperform a series of works. The output data includes a human operationcorresponding to the case or a movement of the robot 10 caused by thehuman operation. The movement switching section 30 acquires control dataon the robot 10 used to make the robot 10 perform the works, theacquisition being made by obtaining output data related to a humanoperation from the learned model 43, the human operation being presumedin response to and in accordance with input data received by the learnedmodel 43, the input data being related to situations of the robot 10 andsurroundings of the robot 10. The completion rate acquisition section 51acquires a completion rate indicating to which progress level in theseries of works the output data outputted by the learned model 43corresponds. The certainty factor acquisition section 52 acquires acertainty factor indicating a probability of the presumption in a casewhere the learned model 43 outputs the output data in response to thereception of the input data.

In this embodiment, the robot is controlled by a robot control methodincluding a control data acquisition step, a completion rate acquisitionstep, and a certainty factor acquisition step, described as follows. Inthe control data acquisition step, control data on the robot used tomake the robot perform the works is acquired by obtaining output datarelated to a human operation from the learned model 43, the humanoperation being presumed in response to and in accordance with inputdata received by the learned model 43, the input data being related tosituations of the robot 10 and surroundings of the robot 10. In thecompletion rate acquisition step, a completion rate is acquired, thecompletion rate indicating to which progress level in the series ofworks the output data outputted by the learned model 43 corresponds. Inthe certainty factor acquisition step, a certainty factor is acquired,the certainty factor indicating a probability of the presumption in acase where the learned model 43 outputs the output data in response tothe reception of the input data.

Based on the completion rate and the certainty factor thus acquired, thereason why the learned model 43 has made such an output in response tothe input can be inferred to some extent by the user. As a result, theblackboxness which is conventionally inherent in the learned model 43can be reduced, so that the user can apply the learned model 43 to arobot control with a sense of conviction. In addition, the user canexamine the learning more appropriately, by using the acquiredcompletion rate and certainty factor as a clue. For example, the usercan easily grasp how the contents learned by the learned model 43 shouldbe corrected, based on the acquired completion rate and certaintyfactor.

The robot control device 15 according to this embodiment furtherincludes the completion rate monitoring section 56 that monitors thecompletion rate acquired by the completion rate acquisition section 51.

This allows the robot control device 15 to easily determine whether ornot the works performed by the robot 10 are progressing favorably.

If, as a result of monitoring by the completion rate monitoring section56, the completion rate is continuously kept less than a predeterminedvalue for a predetermined period or longer, the robot control device 15according to this embodiment stops controlling the robot 10 in themiddle of the works.

This can prevent a wasteful movement.

If, as a result of monitoring by the completion rate monitoring section56, the completion rate is continuously kept less than a predeterminedvalue for a predetermined period or longer, the robot control device 15according to this embodiment controls the robot 10 such that workshaving changed settings are applied from a halfway point.

This can prevent a wasteful movement, and can automatically resume theworks.

In the robot control device 15 according to this embodiment, thecompletion rate is obtained based on a result of clustering aspect datapieces that are data pieces including states of the robot 10 andsurroundings of the robot 10, the states being chronologically acquired.

Accordingly, although the state of the robot 10 and surroundings of therobot 10 changes from moment to moment in the course of the series ofworks, the completion rate can be obtained in consideration ofcharacteristics of each state. Consequently, a progress rate thatproperly represents a progress level can be acquired.

The robot control device 15 according to this embodiment furtherincludes the certainty factor monitoring section 57 that monitors thecertainty factor acquired by the certainty factor acquisition section52.

This can make it easy for the robot control device 15 to determinewhether or not a situation that cannot be easily expected by theexisting learning is occurring.

The robot control device 15 according to this embodiment generatesinformation indicating the input data received by the learned model 43when the certainty factor is equal to or less than a predeterminedvalue.

This can make it easy to prepare information, etc. necessary foradditional learning, for example.

The robot control device 15 according to this embodiment is able tostart controlling the robot 10 based on the output data outputted by thelearned model 43, from a state where the certainty factor is equal to orgreater than a predetermined value, the state corresponding to a halfwaypoint in the series of works.

Since it is possible that the works learned by the learned model 43 areperformed only partially, the scope of use of the learned model 43 canbe widened. Moreover, whether or not to use the output from the learnedmodel 43 is switched only in a situation having a large certaintyfactor, and therefore it is less likely that a movement of the robot 10unexpected by the user is performed.

The robot control device 15 according to this embodiment is able toterminate the control of the robot 10 based on the output data outputtedby the learned model 43, in a state where the certainty factor is equalto or greater than a predetermined value, the state corresponding to ahalfway point in the series of works.

Since it is possible that the works learned by the learned model 43 areperformed only partially, the scope of use of the learned model 43 canbe widened. Moreover, whether or not to use the output from the learnedmodel 43 is switched only in a situation having a large certaintyfactor, and therefore a movement of the robot 10 unexpected by the useris less likely to be performed.

The robot control device 15 according to this embodiment is able toacquire control data on the robot 10 used to make the robot 10 performthe works based on the output data outputted by two or more learnedmodels 43 that are created each corresponding to each of differentseries of works. The robot control device 15 is able to control therobot 10 such that the robot 10 performs coupled works in which thedifferent series of works are chronologically coupled, by successivelyperforming controls of the robot 10 based on the output data outputtedrespectively by the two or more learned models 43, a boundary betweenthe successive controls being a state where the certainty factor isequal to or greater than a predetermined value.

This makes it easy to perform a complicated movement of the robot 10. Inaddition, a to-be-used output of the learned model 43 is switched onlyin a situation having a large certainty factor, and therefore a movementof the robot 10 unexpected by the user is less likely to be performed.

The robot control device 15 according to this embodiment is able tooutput an associated correspondence of: data related to situations ofthe robot 10 and surroundings of the robot 10, the data being receivedby the learned model 43; data related to a movement of the robot 10based on an output from the learned model 43; and at least either of thecompletion rate or the certainty factor. The associated correspondenceis outputted as a movement log, for example.

This can provide information useful to examine and evaluate the movementof the robot 10, for example.

In the robot control device 15 according to this embodiment, the learnedmodel 43 is capable of expressing a state transition, and capable ofoutputting a movement corresponding to each state. The robot controldevice 15 further includes a soundness factor acquisition section 53that acquires a soundness factor indicating the degree to which a staterecognized by the learned model 43 is preferable for the user.

Accordingly, the user can further obtain information useful to infer thereason why the learned model 43 has made an output in response to aninput.

The robot control device 15 according to this embodiment is capable ofoutputting a movement log including the input data received by thelearned model 43 and data related to a movement of the robot 10 based onthe output data outputted by the learned model 43. With respect to amovement of the robot 10 described in the movement log, the user isallowed to give an evaluation for each movement corresponding to a partof the series of works. Based on the evaluation given by the user, therobot control device 15 adjusts a soundness factor corresponding to thestate in the learned model 43.

Accordingly, by partially evaluating the movements of the robot 10, theuser can adjust the control so as to increase the likelihood ofperforming a preferable movement. This makes it easier to provide asense of conviction about the robot control, even with use of thelearned model 43.

After adjusting the soundness factor corresponding to the state based onthe evaluation given by the user, the robot control device 15 accordingto this embodiment performs at least either of adjustment of a parameterof the learned model 43 or reinforcement learning such that the statewhere the soundness factor is high can be obtained.

Accordingly, a control of the robot 10 desired by the user can beperformed easily.

In the robot control device 15 according to this embodiment, themovement log includes the soundness factor acquired by the soundnessfactor acquisition section 53.

This allows the user to refer to the soundness factor when partiallyevaluating the movements of the robot 10. Accordingly, the evaluationcan be made properly, and therefore a control of the robot 10 desired bythe user can be performed efficiently.

The robot control device 15 according to this embodiment is able tooutput an associated correspondence of: the input data received by thelearned model 43; data related to a movement of the robot 10 based onthe output data outputted by the learned model 43; and the soundnessfactor.

This can provide information useful to examine and evaluate the movementof the robot 10, for example.

In this embodiment, the robot system 1 includes the robot control device15 and the robot 10.

Accordingly, the robot system 1 that can easily provide the user with asense of conviction about the robot movement can be achieved.

While a preferred embodiment of the present invention has been describedabove, the above-described configurations may be modified, for example,as follows.

Ranges of values that can be taken by the completion rate, the certaintyfactor, and the soundness factor are optional, and for example, they canbe from 0 to 1.

The learned model 43 may be configured to learn the relationship betweenthe sensor information and the movement instruction given to the robot10, instead of learning the relationship between the sensor informationand the user operation force.

In the above-described embodiment, the robot control device 15re-creates the learned model 43 by adjusting the value of the soundnessfactor based on the evaluation value set by the user. The robot controldevice 15, alternatively, may be configured to re-create the learnedmodel 43 (in other words, to correct the learned model 43) throughreinforcement learning with use of the evaluation value set by the user.In this configuration, the evaluation value is used as a reward inperforming the reinforcement learning.

A sensor that is not any of the movement sensor 11, the force sensor 12,and the camera 13 may be used as a sensor (state sensor) for acquiringstates of the robot 10 and surroundings of the robot 10.

The robot system 1 may be configured such that the operation section 21serves as a master arm used for a remote control while the robot 10serves as a slave arm. In such a configuration, the AI section 40 cancreate the learned model 43 that has undergone learning based on user'soperations on the master arm.

REFERENCE SIGNS LIST

1 robot system

10 robot

11 movement sensor

12 force sensor

13 camera

15 robot control device

30 movement switching section (control data acquisition section)

43 learned model

51 completion rate acquisition section

52 certainty factor acquisition section

1. A robot control device comprising: a learned model created throughlearning work data composed of input data and output data, the inputdata including states of a robot and surroundings of the robot in a casewhere human operates the robot to cause the robot to perform a series ofworks, the output data including a human operation corresponding to thecase or a movement of the robot caused by the human operation; a controldata acquisition section that acquires control data on the robot used tomake the robot perform the works, the acquisition being made byobtaining output data related to a human operation or a movement of therobot from the learned model, the human operation or the movement of therobot being presumed in response to and in accordance with input datareceived by the learned model, the input data being related to states ofthe robot and surroundings of the robot; a completion rate acquisitionsection that acquires a completion rate indicating to which progresslevel in the series of works the output data outputted by the learnedmodel corresponds; and a certainty factor acquisition section thatacquires a certainty factor indicating a probability of the presumptionin a case where the learned model outputs the output data in response toreception of the input data.
 2. The robot control device according toclaim 1, further comprising a completion rate monitoring section thatmonitors the completion rate acquired by the completion rate acquisitionsection.
 3. The robot control device according to claim 2, wherein if,as a result of monitoring by the completion rate monitoring section, thecompletion rate is continuously kept less than a predetermined value fora predetermined period or longer, the robot control device stopscontrolling the robot in the middle of the works.
 4. The robot controldevice according to claim 2, wherein if, as a result of monitoring bythe completion rate monitoring section, the completion rate iscontinuously kept less than a predetermined value for a predeterminedperiod or longer, the robot control device controls the robot such thatworks having changed settings are applied from a halfway point.
 5. Therobot control device according to claim 1, wherein the completion rateis obtained based on a result of clustering data pieces including statesof the robot and surroundings of the robot, the states beingchronologically acquired.
 6. The robot control device according to claim1, further comprising a certainty factor monitoring section thatmonitors the certainty factor acquired by the certainty factoracquisition section.
 7. The robot control device according to claim 6,wherein the robot control device generates information indicating theinput data received by the learned model when the certainty factor isequal to or less than a predetermined value.
 8. The robot control deviceaccording to claim 1, wherein the robot control device is capable ofstarting to control the robot based on the output data outputted by thelearned model, from a state where the certainty factor is equal to orgreater than a predetermined value, the state corresponding to a halfwaypoint in the series of works.
 9. The robot control device according toclaim 1, wherein the robot control device is capable of terminating thecontrol of the robot based on the output data outputted by the learnedmodel, in a state where the certainty factor is equal to or greater thana predetermined value, the state corresponding to a halfway point in theseries of works.
 10. The robot control device according to claim 1,wherein the robot control device is capable of acquiring control data onthe robot used to make the robot perform the works based on the outputdata outputted by two or more learned models that are created eachcorresponding to each of different series of works, and the robotcontrol device is capable of controlling the robot such that the robotperforms coupled works in which the different series of works arechronologically coupled, by successively performing controls of therobot based on the output data outputted respectively by the two or morelearned models, a boundary between the successive controls being a statewhere the certainty factor is equal to or greater than a predeterminedvalue.
 11. The robot control device according to claim 1, wherein therobot control device is capable of outputting an associatedcorrespondence of: data related to states of the robot and surroundingsof the robot, the data being received by the learned model; data relatedto a movement of the robot based on an output from the learned model;and at least either of the completion rate or the certainty factor. 12.The robot control device according to claim 1, wherein the learned modelis capable of expressing a state transition, and capable of outputting amovement corresponding to each state, and the robot control devicefurther comprises a soundness factor acquisition section that acquires asoundness factor indicating the degree to which a state recognized bythe learned model is preferable for a user.
 13. The robot control deviceaccording to claim 12, wherein the robot control device is capable ofoutputting a movement log, the movement log including the input datareceived by the learned model and data related to a movement of therobot based on the output data outputted by the learned model, withrespect to a movement of the robot described in the movement log, theuser is allowed to give an evaluation for each movement corresponding toa part of the series of works, and based on the evaluation given by theuser, the robot control device adjusts a soundness factor correspondingto the state in the learned model.
 14. The robot control deviceaccording to claim 13, wherein after adjusting the soundness factorcorresponding to the state based on the evaluation given by the user,the robot control device performs at least either of adjustment of aparameter of the learned model or reinforcement learning such that thestate where the soundness factor is high can be obtained.
 15. The robotcontrol device according to claim 14, wherein the movement log includesthe soundness factor acquired by the soundness factor acquisitionsection.
 16. The robot control device according to claim 12, wherein therobot control device is capable of outputting an associatedcorrespondence of: the input data received by the learned model; datarelated to a movement of the robot based on the output data outputted bythe learned model; and the soundness factor.
 17. A robot systemcomprising: the robot control device according to claim 1; and therobot.
 18. A robot control method using a learned model that denotes amodel created through learning work data composed of input data andoutput data, the input data including states of a robot and surroundingsof the robot in a case where human operates the robot to cause the robotto perform a series of works, the output data including a humanoperation corresponding to the case or a movement of the robot caused bythe human operation, the robot control method comprising: a control dataacquisition step of acquiring control data on the robot used to make therobot perform the works, by obtaining output data related to a humanoperation or a movement of the robot from the learned model, the humanoperation or the movement of the robot being presumed in response to andin accordance with input data received by the learned model, the inputdata being related to states of the robot and surroundings of the robot;a completion rate acquisition step of acquiring a completion rateindicating to which progress level in the series of works the outputdata outputted by the learned model corresponds; and a certainty factoracquisition step of acquiring a certainty factor indicating aprobability of the presumption in a case where the learned model outputsthe output data in response to reception of the input data.